Senior Data Platform Engineer

Role overview

Build and operate a shared data platform that turns information from multiple business units into reliable products for analytics, operations, and AI. The role combines hands-on platform engineering with architectural leadership, governance, and close work with analytics and machine learning teams.

Responsibilities - Manage and improve a data stack spanning Databricks, Airflow, dbt, GitLab, and AWS, including platform performance and capability upgrades. - Design scalable data products and ETL/ELT pipelines for structured and unstructured data, with dependable availability and timely delivery to downstream users. - Set secure, cost-aware architecture and practices for data modeling, orchestration, and observability. - Establish data quality controls, lineage, metadata management, versioning, and schema evolution to support a trusted semantic layer. - Work with analytics, ML, and AI teams to translate their requirements into technical solutions.

Requirements - At least seven years of experience in data engineering or data management, with a record of delivering stable, high-performance solutions. - Hands-on Databricks experience, including PySpark, Delta Lake, and Unity Catalog. - Advanced SQL and strong knowledge of dimensional modeling, star schemas, and snowflake schemas. - Experience with a major cloud platform and orchestration tools such as Airflow or dbt. - Experience with relational databases, Docker or Kubernetes, CI/CD, data versioning, and schema evolution. - A master’s or doctoral degree in computer science, a STEM field, or a related quantitative discipline.

Benefits and work setup

Applicants must be eligible to work in the country of application. Relocation support and visa sponsorship are unavailable. The description emphasizes respectful, open collaboration and inclusion.